A node degree dependent random perturbation method for prediction
of missing links in the network
WenJun Zhang
School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; International Academy of Ecology and
Environmental Sciences, Hong Kong
Network Biology
ISSN 2220-8879
http://www.iaees.org/publications/journals/nb/online-version.asp
2016
6
1
1
11
International Academy of Ecology and Environmental Sciences
Hong Kong
25 September 2015
27 October 2015
1 March 2016
missing links
network
rules
node degree
random perturbation
prediction
likelihood
In present study, I proposed a node degree dependent random perturbation algorithm for prediction of missing links in the network. In the algorithm, I assume that a node with more existing links harbors more missing links. There are two rules. Rule 1 means that a randomly chosen node tends to connect to the node with greater degree. Rule 2 means that a link tends to be created between two nodes with greater degrees. Missing links of some tumor related networks (pathways) are predicted. The results prove that the prediction efficiency and percentage of correctly predicted links against predicted missing links with the algorithm increases as the increase of network complexity. The required number for finding true missing links in the predicted list reduces as the increase of network complexity. Prediction efficiency is complexity-depedent only. Matlab codes of the algorithm are given also. Finally, prospect of prediction for missing links is briefly reviewed. So far all prediction methods based on static topological structure only (represented by adjacency matrix) seems to be low efficient. Network evolution based, node similarity based, and sampling based (correlation based) methods are expected to be the most promising in the future.
DOI 10.0000/issn-2220-8879-networkbiology-2016-v6-0001
http://www.iaees.org/publications/journals/nb/articles/2016-6(1)/perturbation-method-for-prediction-of-missing-links.pdf